We performed additional ablative experiments to quantitatively illustrate the importance of main grid components and attention mechanisms, by retraining the model each time without specific layer(s). We tested the impact of two components: the Grid-LSTM handling static grid shown earlier in Figure REF  and the soft-attention mechanism explained through Eq. REF . We followed the same k-fold cross-validation used for training the original MCR model on the same datasets: Zara and UCY.
Table REF  clearly illustrates the improvement of our basic model, G-LSTM, upon the NoFrustum model [1]}. Our basic model only deploys one Grid-LSTM cell and relies on pedestrians positional trajectories. This design complexity resembles the NoFrustum model which is a simpler variant of MX-LSTM work. They only use pedestrians positions with LSTM cell. The usage of Grid-LSTM attains outperforming results over Zara and UCY in a row.
We picked the NoFrustum variant as a baseline to compare with our basic model design G-LSTM, which excludes the GNN and SRI units from its pipeline and only take positional trajectories into single Grid-LSTM cell.
